In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is.
The coding challenge for this video is here:
https://github.com/llSourcell/twitter_sentiment_challenge
Naresh's winning code from last episode:
https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py
Victor's Runner up code from last episode:
https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
More on TextBlob:
https://textblob.readthedocs.io/en/dev/
Great info on Sentiment Analysis:
https://www.quora.com/How-does-sentiment-analysis-work
Great sentiment analysis api:
http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis
Read over these course notes if you wanna become an NLP god:
http://cs224d.stanford.edu/syllabus.html
Best book to become a Python god:
https://learnpythonthehardway.org/
Please share this video, like, comment and subscribe!
That's what keeps me going.
Feel free to support me on Patreon:
https://www.patreon.com/user?u=3191693
Two Minute Papers Link:
https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Link to the full Kaggle tutorial w/ code: https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
Sentiment Analysis in 5 lines of code:
http://blog.dato.com/sentiment-analysis-in-five-lines-of-python
I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/
The Stanford Natural Language Processing course: https://class.coursera.org/nlp/lecture
Cool API for sentiment analysis: http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis
I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

( Data Science Training - https://www.edureka.co/data-science )
This Sentiment Analysis Tutorial shall give you a clear understanding as to how a Sentiment Analysis machine learning algorithm works in R. Towards the end, we will be streaming data from Twitter and will do a comparison between two football teams - Barcelona and Real Madrid (El Clasico Sentiment Analysis)
Below are the topics covered in this tutorial:
1) What is Machine Learning?
2) Why Sentiment Analysis?
3) What is Sentiment Analysis?
4) How Sentiment Analysis works?
5) Sentiment Analysis - El Clasico Demo
6) Sentiment Analysis - Use Cases
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Data Science playlist here: https://goo.gl/60NJJS
#SentimentAnalysis #Datasciencetutorial #Datasciencecourse #datascience
How it Works?
1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. You will get Lifetime Access to the recordings in the LMS.
4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Customer Reviews:
Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

In this video we work on an actual sentiment analysis dataset (which is an instance of text classification), for which I also provide Python code (see below). The approach is very similar to something that is commonly called a Naive Bayes Classifier.
Website associated with this video:
http://karpathy.ca/mlsite/lecture2.php

University of Southern California Institute for Creative Technologies computer scientist Louis-Philippe Morency is analyzing online videos to capture the nuances of how people communicate opinions through words and actions.
For Morency, who is also research assistant professor at the USC Viterbi School of Engineering, online videos are the latest tool in the growing field of opinion mining. In his current research -- figuring out how to identify when someone is sharing a positive, negative or neutral opinion - YouTube provides a limitless library of likes and loathes.
Morency and his colleagues created a proof-of-concept data set of about 50 YouTube videos that feature people expressing their opinions. The videos were input into a computer program Morency developed that zeroes in on aspects of the speaker's language, speech patterns and facial expressions to determine the type of opinion being shared.
Morency's small sample has already identified several advantages to analyzing gestures and speech patterns over looking at writing alone. First, people don't always use obvious polarizing words like love and hate each time they express an opinion. So software programmed to search for these "obvious" occurrences can miss many other valuable posts.
Also, Morency found that people smile and look at the camera more when sharing a positive view. Their voices become higher pitched when they have a positive or negative opinion, and they start to use a lot more pauses when they are neutral.
"These early findings are promising but we still have a long way to go," said Morency. "What they tell us is that what you say, how you say it, and the gestures you make while speaking all play a role in pinpointing the correct sentiment."
Morency first demonstrated his YouTube model at the International Conference on Multimodal Interaction in Spain last fall. He has since expanded the data set to include close to 500 videos and will submit results from this larger sample for publication later this year.
The YouTube opinion data set is also available to other researchers by contacting Morency's Multimodal Communication and Machine Learning lab at ICT. Potential commercial uses could include for marketing or survey purposes. In the academic community, Morency foresees his research and database being resources for scientists working to understand human non-verbal and verbal communication, helping to identify conditions like autism or depression or to build more engaging educational systems.
For more information go to: http://multicomp.ict.usc.edu/

Welcome to Data Lit! This 3-month course is an intro to data science for beginners. In this video, I'll explain how a popular data science technique called sentiment analysis works using a real-world scenario. We'll play the role of a data scientist working at a startup making a personal healthcare device. Using sentiment analysis, we'll understand how consumers feel about a competitors product. That'll help us make decisions on how to promote our own product, and what feature we can focus on the most. Using Python, Twitter, and Google Colab, anyone can do this process in just a few minutes. Enjoy!
Code for this video:
https://github.com/llSourcell/Sentiment_Analysis
Please Subscribe! And Like. And comment. That's what keeps me going.
Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology
Join us at the School of AI:
https://theschool.ai/
More learning resources:
https://towardsdatascience.com/sentiment-analysis-with-python-part-1-5ce197074184
https://www.geeksforgeeks.org/twitter-sentiment-analysis-using-python/
https://www.datacamp.com/community/tutorials/simplifying-sentiment-analysis-python
https://www.kaggle.com/ngyptr/python-nltk-sentiment-analysis
https://pythonspot.com/python-sentiment-analysis/
https://www.analyticsvidhya.com/blog/2018/07/hands-on-sentiment-analysis-dataset-python/
Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/
Please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
#DataLit #SchoolOfAI #SirajRaval
Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

Author:
Vita Markman, LinkedIn Corporation
Abstract:
Quality training data is essential for building high performance machine learning models. However, certain types of tasks such as opinion mining are inherently subjective, making it hard to elicit reliable judgements from human annotators. The problem is further exacerbated in situations where opinions are elicited on short text such as Tweets or micro reviews containing only one or two lines. The talk addresses various means of circumventing these challenges via automation of some annotation tasks as well as setting up multiple experiments for collecting human judgements.
More on http://www.kdd.org/kdd2016/
KDD2016 Conference is published on http://videolectures.net/

This tutorial will walk you through three different types of Sentiment application to a data set. It will strip text into single words and allow you to apply a sentiment match to each word (if its available in R). We use the three sentiments; bing, nrc, & afinn.
Connect to SQL Server:
https://youtu.be/DwzIx7CEn0Y
Create data set:
https://github.com/ProfessorPitch/ProfessorPitch/blob/master/SQL/Sentiment.sql
Sentiment Script:
https://github.com/ProfessorPitch/ProfessorPitch/blob/master/R/Sentiment.R

Finally, the moment we've all been waiting for and building up to. A live test!
We've decided to employ this classifier to the live Twitter stream, using Twitter's API.
We've already covered how to do live Twitter API streaming, if you missed it, you can catch up here: http://pythonprogramming.net/twitter-api-streaming-tweets-python-tutorial/
After this, we output the findings to a text file, which we intend to graph!
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Speaker: Mimansa Jaiswal
Description
I aim to cover the following aspects under the talk: 1. Using nltk with python (Overview of modules and data) 2. Basics of natural language processing (tokenisation, stemming, wordnet, pos tagging) 3. Sentiment Analysis (overview of classification methods, binary versus fuzzy classification) 4. Directions of sentiment analysis 5. Applications in discerning human emotions.
Abstract
The workshop would aim to provide a general overview of the concepts that are used in conducting a Sentiment Analysis on textual data.
The beginning 5 minutes of the talk would deal with how nltk is used in python, what corpus it provides, the stemmers inbuilt, sentence tokenisation and pickled models. I would then move to using this nltk toolkit for sentence tokenisation and pos tagging and how NER (Named-Entity Recognition can be useful for Aspect based sentiment analysis) which would take around 10 minutes.
I would then proceed to discuss about the classification methods like bag-of-words, random forests etc. and where and when they should be used. In here, I would also explain the bias induced in dataset regarding the industry it is dealing with. I would also touch briefly on binary classification (positive, negative) or probability value vector in case of multi-label classification. This would take 10 minutes.
I would then discuss about the various directions in which sentiment analysis is used, namely, stance detection, aspect based sentiment analysis etc. I would go over the various ares that sentiment analysis can be used (product reviews, social media posts) and how that information about sentiment can be used. And then I would conclude by discussing about the projects that I have worked upon, that is, giving AI the benefit of recognising and empathising with emotions and how it would be helpful.
Event Page: https://pycon.sg
Produced by Engineers.SG
Help us caption & translate this video!
http://amara.org/v/P6SN/

Full Python + Pandas + Sentiment analysis Playlist: http://www.youtube.com/watch?v=0ySdEYUONz0&list=PLQVvvaa0QuDdktuSQRsofoGxC2PTSdsi7&feature=share
This series uses python with Pandas for data analysis. Our data set will be a database dump from Sentdex.com sentiment analysis, containing about 600 stocks, mostly S&P 500 stocks.
Pandas is used to work with our data quickly and efficiently. The ideas of Pandas is to act as a sort of framework for quickly analyzing data and modeling it.
Sentiment Analysis data:
http://sentdex.com/downloads/stocks_sentdex.csv.gz
Python Module downloads:
(Get all of the listed dependencies, or at least the major ones like NumPy, Dateutils, Matplotlib, )
http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas
https://www.python.org/downloads/
http://matplotlib.org/downloads.html
http://www.numpy.org/
http://seaofbtc.com
http://sentdex.com
http://hkinsley.com
https://twitter.com/sentdex
Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6

Full Python + Pandas + Sentiment analysis Playlist: http://www.youtube.com/watch?v=0ySdEYUONz0&list=PLQVvvaa0QuDdktuSQRsofoGxC2PTSdsi7&feature=share
This video tutorial is dedicated to teaching the basics of using Pandas with Python. In this example we grab stock prices from Yahoo Finance, learn how to access specific columns, how to modify columns, add columns, delete columns, and perform basic math on them.
This series uses python with Pandas for data analysis. Our data set will be a database dump from Sentdex.com sentiment analysis, containing about 600 stocks, mostly S&P 500 stocks.
Pandas is used to work with our data quickly and efficiently. The ideas of Pandas is to act as a sort of framework for quickly analyzing data and modeling it.
Sentiment Analysis data:
http://sentdex.com/downloads/stocks_sentdex.csv.gz
Matplotlib Styles video: https://www.youtube.com/watch?v=WmhdQdx8Gjo
Python Module downloads:
(Get all of the listed dependencies, or at least the major ones like NumPy, Dateutils, Matplotlib, )
http://www.lfd.uci.edu/~gohlke/pythonlibs/#pandas
https://www.python.org/downloads/
http://matplotlib.org/downloads.html
http://www.numpy.org/
http://seaofbtc.com
http://sentdex.com
http://hkinsley.com
https://twitter.com/sentdex
Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6

Sentiment analysis has been for long poster boy problem of NLP and has attracted a lot of research. However, despite so much work in this sub area, most sentiment analysis models fail miserably in handling sarcasm. Rise in usage of sentiment models for analysis social data has only exposed this gap further. Owing to the subtilty of language involved, sarcasm detection is a hard problem.
Most attempts at sarcasm detection still depend on hand crafted features which are dataset specific. In this talk we see some of the very recent attempts to leverage recent advances in NLP for building generic models for sarcasm detection.
Key take aways:
+ Challenges in sarcasm detection
+ Deep dive into a end to end solution using DL to build generic models for sarcasm detection
+ Short comings and road forward
Anuj is currently working as Independent Researcher. In past he was Director - Machine Learning at Huawei Technologies. He has headed ML efforts at a bunch of organizations. Prior to that, he dropped out of Phd to work with startups, completed his master’s with a specialization in theoretical computer science.
Speaker at various forums like Anthill, Nvidia forums, PyData, Fifth Elephant, ICDCN, PODC.
More about him - https://www.linkedin.com/in/anuj-gupta-15585792/

This tutorial video covers how to do real-time analysis alongside your streaming Twitter API v1.1 feed. In this case, for example, we use the Sentdex Sentiment Analysis API, http://sentdex.com/sentiment-analysis-api/, though you can use ANY API like this, or just your own custom function too.
If you don't already have a twitter stream set up, here is some sample code and tutorial video for it: http://sentdex.com/sentiment-analysisbig-data-and-python-tutorials-algorithmic-trading/how-to-use-the-twitter-api-1-1-to-stream-tweets-in-python/
Sentdex.com
Facebook.com/sentdex
Twitter.com/sentdex

http://socioware.de
https://www.researchgate.net/publication/278383087_Opinion_Mining_and_Lexical_Affect_Sensing
EmoText for opinion mining in long texts illustrates a domain-independent approach to opinion mining. A thorough description is available in the book "Opinion mining and lexical affect sensing".
Empirically revealed that texts should contain not less than 200 words for reliable classification. The engine evaluates features (lexical, stylometric, grammatical, deictic) using different evaluation methods and uses the SMO or NaiveBayes classifiers from the WEKA data mining toolkit for text classification. Statistical EmoText formed a basis for the statistical framework for experimentation and rapid prototyping. The approach was tested on the following English corpora: a Pang corpus with weblogs, Berardinelli movie review corpus with movie reviews, a corpus with spontaneous dialogues (the SAL corpus), and a corpus with product reviews.

After some consideration it became clear that a new dataset would solve a lot of problems. This tutorial covers employing a new dataset, and what is involved in this process.
This time, we're using a movie reviews data set that contains much shorter movie reviews.
You can get this data set from: http://pythonprogramming.net/static/downloads/short_reviews/
This one yields us a far more reliable reading across the board, and is far more fitting for the tweets we intend to read from the Twitter API soon.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Made by Natansh Prasad (101411024 ).
This is one of my first college projects. It is a surprise that it even works. So you are better off looking for a better source of code. The explanation is up to the mark though.
If you are working on sentiment analysis then it is better to use Deep Learning (LSTM) or even CNN.
There are many good resources on YT. Try to look for them (Siraj Raval, Tanmay Bakshi). I am not currently working in the field of machine learning so I can't help you much more.
Hope this helps. Keep learning.
Dataset From: http://ai.stanford.edu/~amaas/data/sentiment/

Twitter Mining with R part 1 takes you through setting up a connection with Twitter. This requires a couple packages you will need to install, and creating a Twitter application, which needs to be authorized in R before you can access tweets. We quickly go through this entire process which may take some flexibility on your part so be patient and be ready troubleshoot as details change with updates.
Warning: You are going to face challenges setting up the twitter API connection. The steps for this part have been known to change slightly over time for a variety of reasons. Follow the general steps and expect a few errors along the way which you will have to troubleshoot. It is hard to solve these issues remotely from where I am.

Welcome to part five of the Deep Learning with Neural Networks and TensorFlow tutorials. Now that we've covered a simple example of an artificial neural network, let's further break this model down and learn how we might approach this if we had some data that wasn't preloaded and setup for us. This is usually the first challenge you will come up against afer you learn based on demos. The demo works, and that's awesome, and then you begin to wonder how you can stuff the data you have into the code. It's always a good idea to grab a dataset from somewhere, and try to do it yourself, as it will give you a better idea of how everything works and what formats you need data in.
Positive data: https://pythonprogramming.net/static/downloads/machine-learning-data/pos.txt
Negative data: https://pythonprogramming.net/static/downloads/machine-learning-data/neg.txt
https://pythonprogramming.net
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

Provides steps for applying Naive Bayes Classification with R.
Data: https://goo.gl/nCFX1x
R file: https://goo.gl/Feo5mT
Machine Learning videos: https://goo.gl/WHHqWP
Naive Bayes Classification is an important tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

This study attempts to discover and analyze the predictive power of stock messages, posting on financial message boards, on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Our dataset consist of 447'393 messages, on the 30 Dow Jones Index (DJIA) stocks, posted on the Yahoo! Finance message board in the period August 2012 to May 2013, of which 55'217 with sentiment tag and 5'967 distinct authors.
We propose a novel way to generate sentiment based on author's credibility, calculated on accuracy of his past messages. Our results provide empirical evidence that, using our method (3 and 5 scale index models), there is strong and useful information on financial message boards pertinent to stock market movements. In addition, we demonstrate that we can use this information in order to make accurate predictions about the return on investment and to implement good trading strategies based on sentiment analysis, doing, on average, much better than traditional investment strategies like Buy and Hold or Moving Averages (5-20 periods).
Our results appear to be statistically and economically significant. Theory that suggests a link between small investor behavior and stock market performance is now supported by our work.

Author:
Brian Keith, Universidad Católica del Norte
Abstract:
Sentiment analysis and opinion mining is an area that has experienced considerable growth over the last decade. This area of research attempts to determine the feelings, opinions, emotions, among other things, of people on something or someone. To do this, techniques from natural language processing and machine learning algorithms are mainly used. This article discusses the problem of determining the polarity of reviews using a novel ordinal classification technique called Barycentric Coordinates for Ordinal Classification (BCOC). The aim of this analysis is to explore the viability of application of BCOC on the field of sentiment analysis. This new method is based on the hypothesis that the ordinal classes can be represented geometrically inside a convex polygon on the real plane by using barycentric coordinates. A set of experiments were conducted to evaluate the capability and performance of the proposed approach relative to a baseline, using accuracy as the general measure of performance. The experiments include testing on generic ordinal classification data sets and on multi-class sentiment analysis data sets. In general the method is competitive with the state of the art. The results show no significant difference over the baseline in the case of generic ordinal classification and sentiment analysis with three classes. However, in the case of sentiment analysis with four classes the results show improvements in the overall accuracy.
More on http://www.kdd.org/kdd2017/
KDD2017 Conference is published on http://videolectures.net/

Weakly-supervised Deep Embedding for Product Review Sentiment Analysis in Python
To get this project in ONLINE or through TRAINING Sessions, Contact:
JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.
Mobile: (0)9952649690,
Email: [email protected],
Website: https://www.jpinfotech.org
Product reviews are valuable for upcoming buyers in helping them make decisions. To this end, different opinion mining techniques have been proposed, where judging a review sentence’s orientation (e.g. positive or negative) is one of their key challenges. Recently, deep learning has emerged as an effective means for solving sentiment classification problems. A neural network intrinsically learns a useful representation automatically without human efforts. However, the success of deep learning highly relies on the availability of large-scale training data. We propose a novel deep learning framework for product review sentiment classification which employs prevalently available ratings as weak supervision signals. The framework consists of two steps: (1) learning a high level representation (an embedding space) which captures the general sentiment distribution of sentences through rating information; (2) adding a classification layer on top of the embedding layer and use labeled sentences for supervised fine-tuning. We explore two kinds of low level network structure for modeling review sentences, namely, convolutional feature extractors and long short-term memory. To evaluate the proposed framework, we construct a dataset containing 1.1M weakly labeled review sentences and 11,754 labeled review sentences from Amazon. Experimental results show the efficacy of the proposed framework and its superiority over baselines.
#python #deeplearning #pythonprojects

In our final year project, we have used VADER for sentiment analysis first, and then we have used our own classification method using basic neural network to first classify suspicious-clear-hazy reviews. Then we have annotated the review with the same along with the polarity of it for user information. Thus user knows if it is positive spam or negative spam.

Demonstration of a project in CS 5593 Data Mining in Fall 2015 at the University of Oklahoma for the Classification of Online News Popularity based on the "Online News Popularity Data Set" in the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Online+News+Popularity).
The project was developed by Maxime Brisse, Aitor Algorta and Sven Erik Jeroschewski.

Customers are from Mars, Managers are from Venus: Deriving Customer Satisfaction Drivers from Online Reviews
The Internet is host to many sites that collect vast amounts of opinions about products and services. These opinions are expressed in written language, and automatic analysis of the written opinions is known as sentiment analysis or opinion mining. In this paper, the written opinions constitute unstructured input data, which we first transform into semi-structured data using an automated framework for aspect-level sentiment analysis. Second, we model the overall customer satisfaction using a Bayesian approach based on the individual aspect rating of each review. Our probabilistic method enables us to discover the relative importance of each aspect for each individual product or service. Empirical experiments on a data set of online reviews of California State Parks, obtained from tripadvisor.com, show the effectiveness of the proposed framework as applied to the aspect-level sentiment analysis and modeling of customer satisfaction with an accuracy of 88.3% in terms of finding the significant aspects.
PAPER: 16